{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import collections\n",
    "\n",
    "import matplotlib.mlab as mlab  \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from matplotlib import font_manager as fm\n",
    "from  matplotlib import cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "sh1 = pd.read_json('data/SH_ty2.json')\n",
    "wx1 = pd.read_json('data/WX_yao.json')\n",
    "wx = {y: sx for sx, yao in wx1.loc[\"药物\"].iteritems() for y in yao}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Shanghan():\n",
    "    def __init__(self):\n",
    "        self.sh1 = pd.read_json('data/SH_ty2.json')\n",
    "        self.wx1 = pd.read_json('data/WX_yao.json')\n",
    "        self.wx = {y: sx for sx, yao in self.wx1.loc[\"药物\"].iteritems() for y in yao}\n",
    "        \n",
    "    def count_fang(self,fang):\n",
    "        a = self.sh1.loc['名'].str.match(fang).sum()\n",
    "        print(a)\n",
    "        \n",
    "    def count_fang_all(self):\n",
    "        l1 = {}\n",
    "        for i in self.sh1.loc['名'].items():\n",
    "            l1[i[1]] = self.sh1.loc['名'].str.match(i[1]).sum()\n",
    "        l2 = sorted(l1.items(),key = lambda item: item[1],reverse=True)\n",
    "        print(l2)\n",
    "        \n",
    "    def count_yao(self,yao):\n",
    "        q1 = self.sh1.loc['方']\n",
    "        q2 = q1[q1.apply(lambda x:yao in x)].count()\n",
    "        print(q2)\n",
    "        \n",
    "    def count_yao_all(self):\n",
    "        q1 = self.sh1.loc['方']\n",
    "        l1 = {}\n",
    "        for i ,j in self.sh1.loc['方'].iteritems():\n",
    "            for g in j:\n",
    "                l1[g]= q1[q1.apply(lambda x:g in x)].count()\n",
    "        l2 = sorted(l1.items(),key = lambda item: item[1],reverse=True)\n",
    "                \n",
    "        print(l2)\n",
    "        \n",
    "    def draw_dir_all(self):#画每一个方剂的方向图\n",
    "        plt.rcParams['figure.figsize'] = [18,13]\n",
    "        plt.style.use('fivethirtyeight')\n",
    "        fang = self.sh1.loc['方']\n",
    "        sx = [self.wx.get(y,\"暂不明\") for f in fang for y in f]\n",
    "        sx_len = len(sx)\n",
    "        qq = {y: cnt / sx_len for y, cnt in collections.Counter(sx).items()}\n",
    "        qq1 = sorted(qq.items(),key = lambda item: item[1], reverse=True)\n",
    "        ss = {}\n",
    "        for i in qq1:\n",
    "            #print(i)\n",
    "            ss[i[0]]=i[1]\n",
    "        \n",
    "        #----------------下面是画饼图-----------------------------------\n",
    "        \n",
    "        labels = list(ss.keys())\n",
    "        X = list(ss.values())\n",
    "        fig, ax = plt.subplots(figsize=(11,11))\n",
    "        \n",
    "        colors = cm.rainbow(np.arange(len(X))/len(X))\n",
    "        #print(X)\n",
    "        patches, texts, autotexts = ax.pie(X, labels=labels, autopct='%1.2f%%',\n",
    "        shadow=False, startangle=170, colors=colors)\n",
    "        \n",
    "        proptease = fm.FontProperties()\n",
    "        proptease.set_size('x-large')\n",
    "        \n",
    "        plt.setp(autotexts, fontproperties=proptease)\n",
    "        plt.setp(texts, fontproperties=proptease)\n",
    "\n",
    "        plt.title(\"伤寒全部方剂五行方向图\", pad = 30)\n",
    "        plt.axis('equal')\n",
    "        plt.show()  \n",
    "        \n",
    "    def count_yao_all_draw(self):\n",
    "        plt.style.use('fivethirtyeight')#这个图的设定字体很清晰\n",
    "        plt.rcParams['figure.figsize'] = [18,13]\n",
    "        #plt.xlabel('药物')\n",
    "        plt.title('伤寒论药物用量排名分析(前10名)', pad = 90)\n",
    "        plt.xticks(fontsize=25)#x轴的字体大小\n",
    "        plt.yticks(fontsize=30)\n",
    "        \n",
    "        \n",
    "        q1 = self.sh1.loc['方']\n",
    "        l1 = {}\n",
    "        t_list = []\n",
    "        n_list = []\n",
    "        for i ,j in self.sh1.loc['方'].iteritems():\n",
    "            for g in j:\n",
    "                l1[g]= q1[q1.apply(lambda x:g in x)].count()\n",
    "        l2 = sorted(l1.items(),key = lambda item: item[1],reverse=True)\n",
    "            \n",
    "        for i in l2[:10]:\n",
    "            t_list.append(i[0])\n",
    "            n_list.append(i[1])\n",
    "            #plt.text(i[1],)\n",
    "        \n",
    "        x = np.arange(10)\n",
    "        ys = [i+x+(i*x)**2 for i in range(10)]\n",
    "        colors = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
    "            \n",
    "        plt.bar(t_list,n_list,color=colors)\n",
    "        plt.show()\n",
    "        \n",
    "        \n",
    "            \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "wx1 = pd.read_json('data/WX_yao.json')\n",
    "wx = {y: sx for sx, yao in wx1.loc[\"药物\"].iteritems() for y in yao}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "fang = sh1.loc['方']\n",
    "sx = [wx.get(y,\"暂不明\") for f in fang for y in f]\n",
    "sx_len = len(sx)\n",
    "qq = {y: cnt / sx_len for y, cnt in collections.Counter(sx).items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "qq1 = sorted(qq.items(),key = lambda item: item[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('向上(木)', 0.2898550724637681),\n",
       " ('中轴(土)', 0.2768115942028985),\n",
       " ('向下(金)', 0.13478260869565217),\n",
       " ('火', 0.11304347826086956),\n",
       " ('暂不明', 0.09420289855072464),\n",
       " ('收藏(水)', 0.09130434782608696)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qq1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('向上(木)', 0.2898550724637681)\n",
      "('中轴(土)', 0.2768115942028985)\n",
      "('向下(金)', 0.13478260869565217)\n",
      "('火', 0.11304347826086956)\n",
      "('暂不明', 0.09420289855072464)\n",
      "('收藏(水)', 0.09130434782608696)\n"
     ]
    }
   ],
   "source": [
    "ss={}\n",
    "for i in qq1:\n",
    "    print(i)\n",
    "    ss[i[0]]=i[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'向上(木)': 0.2898550724637681,\n",
       " '中轴(土)': 0.2768115942028985,\n",
       " '向下(金)': 0.13478260869565217,\n",
       " '火': 0.11304347826086956,\n",
       " '暂不明': 0.09420289855072464,\n",
       " '收藏(水)': 0.09130434782608696}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_items([('向上(木)', 200), ('向下(金)', 93), ('中轴(土)', 191), ('收藏(水)', 63), ('火', 78), ('暂不明', 65)])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collections.Counter(sx).items()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "690"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sx_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_items([('向上(木)', 200), ('向下(金)', 93), ('中轴(土)', 191), ('收藏(水)', 63), ('火', 78), ('暂不明', 65)])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collections.Counter(sx).items()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2898550724637681\n",
      "0.13478260869565217\n",
      "0.2768115942028985\n",
      "0.09130434782608696\n",
      "0.11304347826086956\n",
      "0.09420289855072464\n"
     ]
    }
   ],
   "source": [
    "for y, cnt in collections.Counter(sx).items():\n",
    "    print(cnt/sx_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "q1 = sh1.loc['方']\n",
    "l1 = {}\n",
    "for i ,j in sh1.loc['方'].iteritems():\n",
    "    for g in j:\n",
    "        l1[g]= q1[q1.apply(lambda x:g in x)].count()\n",
    "l2 = sorted(l1.items(),key = lambda item: item[1],reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['1号', '2号', '3号', '4号', '5号', '6号', '7号', '8号', '9号', '10号',\n",
       "       ...\n",
       "       '141号', '142号', '143号', '144号', '145号', '146号', '147号', '148号', '149号',\n",
       "       '150号'],\n",
       "      dtype='object', length=150)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh1.loc['名'].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'桂枝加葛根汤'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh1.T.loc['2号']['名']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = sh1.loc['证'].apply(lambda x: set(x['体证']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = sh1.loc['名'].apply(lambda x: x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = b[b.apply(lambda x: '四逆汤' == x)].index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "d1 = sh1[c]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "原文    {'对应证原文': ['若重发汗。复加烧针者。四逆汤主之。'], '方剂原文': '甘草二两...\n",
       "名                                                   四逆汤\n",
       "方                 {'附子': '1枚', '炙甘草': '2两', '干姜': '1两'}\n",
       "经                               {'阴阳': '阳', '三经': '太阳'}\n",
       "证     {'体证': ['四肢冷', '长期焦虑', '自感体虚', '长期抑郁', '没有精神',...\n",
       "Name: 14号, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "附子     1枚\n",
       "炙甘草    2两\n",
       "干姜     1两\n",
       "dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(d1['方'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "对应证原文                          [若重发汗。复加烧针者。四逆汤主之。]\n",
       "方剂原文                 甘草二两[炙] 干姜一两半 附子一枚[生用 去皮 破八片]\n",
       "服药原文     右三味。以水三升。煮取一升二合。去滓。分温再服。 【强人可大附子一枚。干姜三两。】\n",
       "dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(d1['原文'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "bz = {\"四肢冷\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = sh1.loc['证'].apply(lambda x: set(x['体证']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['14号',\n",
       " '41号',\n",
       " '88号',\n",
       " '118号',\n",
       " '119号',\n",
       " '122号',\n",
       " '123号',\n",
       " '125号',\n",
       " '126号',\n",
       " '127号',\n",
       " '132号',\n",
       " '133号',\n",
       " '143号',\n",
       " '144号',\n",
       " '145号',\n",
       " '146号']"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(a[a.apply(lambda x: bz <= x)].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = a[a.apply(lambda x: bz <= x)].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14号             四逆汤\n",
       "41号             四逆汤\n",
       "88号             四逆汤\n",
       "118号          通脉四逆汤\n",
       "119号            四逆散\n",
       "122号            四逆汤\n",
       "123号            乌梅丸\n",
       "125号          当归四逆汤\n",
       "126号    当归四逆加吴茱萸生姜汤\n",
       "127号            四逆汤\n",
       "132号          通脉四逆汤\n",
       "133号           白头翁汤\n",
       "143号            四逆汤\n",
       "144号       通脉四逆加猪胆汤\n",
       "145号            烧裈散\n",
       "146号          枳实栀子汤\n",
       "Name: 名, dtype: object"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh1[b].loc['名']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = sh1.loc['名'].apply(lambda x: x)\n",
    "c = b[b.apply(lambda x: '桂枝汤' == x)].index[0]\n",
    "d = sh1[c]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'桂枝': '3两', '芍药': '3两', '炙甘草': '2两', '生姜': '3两', '大枣': '12枚'}"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.loc['方']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fixing random state for reproducibility\n",
    "np.random.seed(19680801)\n",
    "\n",
    "# Compute areas and colors\n",
    "N = 150\n",
    "r = 2 * np.random.rand(N)\n",
    "theta = 2 * np.pi * np.random.rand(N)\n",
    "area = 200 * r**2\n",
    "colors = theta\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='polar')\n",
    "c = ax.scatter(theta, r, c=colors, s=area, cmap='hsv', alpha=0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "X = 150 这个区间,从第一个药开始\n",
    "Y = 五行 水在最下,火在最上\n",
    "那可以这样设定\n",
    "水 30, 金 60 土 90 木 120 火 150\n",
    "那么Y也是150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "wx_a = {'水':30,\n",
    "        '金':60,\n",
    "        '土':90,\n",
    "        '木':120,\n",
    "        '火':150  \n",
    "       }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 150)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'水': 30, '金': 60, '土': 90, '木': 120, '火': 150}"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wx_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "fang = sh1.loc['方']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "sx = [wx.get(y,\"暂不明\") for f in fang for y in f]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'炙甘草': '中轴(土)',\n",
       " '人参': '中轴(土)',\n",
       " '大枣': '中轴(土)',\n",
       " '茯苓': '中轴(土)',\n",
       " '胶饴': '中轴(土)',\n",
       " '麦门冬': '中轴(土)',\n",
       " '升麻': '中轴(土)',\n",
       " '瓜蒂': '中轴(土)',\n",
       " '鸡子黄': '中轴(土)',\n",
       " '芍药': '向下(金)',\n",
       " '柴胡': '向下(金)',\n",
       " '杏仁': '向下(金)',\n",
       " '石膏': '向下(金)',\n",
       " '五味子': '向下(金)',\n",
       " '枳实': '向下(金)',\n",
       " '铅丹': '向下(金)',\n",
       " '龙骨': '向下(金)',\n",
       " '牡蛎': '向下(金)',\n",
       " '豉': '向下(金)',\n",
       " '香豉': '收藏(水)',\n",
       " '大黄': '火',\n",
       " '芒硝': '火',\n",
       " '黄连': '火',\n",
       " '黄芩': '火',\n",
       " '泽泻': '火',\n",
       " '厚朴': '火',\n",
       " '通草': '火',\n",
       " '葱白': '火',\n",
       " '地黄': '收藏(水)',\n",
       " '麻黄': '收藏(水)',\n",
       " '葛根': '收藏(水)',\n",
       " '白术': '收藏(水)',\n",
       " '知母': '收藏(水)',\n",
       " '猪苓': '收藏(水)',\n",
       " '栀子': '收藏(水)',\n",
       " '竹叶': '收藏(水)',\n",
       " '生姜': '向上(木)',\n",
       " '附子': '向上(木)',\n",
       " '桂枝': '向上(木)',\n",
       " '半夏': '向上(木)',\n",
       " '干姜': '向上(木)',\n",
       " '粳米': '向上(木)',\n",
       " '细辛': '向上(木)',\n",
       " '阿胶': '向上(木)',\n",
       " '赤小豆': '向上(木)',\n",
       " '蜀椒': '向上(木)'}"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'炙甘草': '中轴(土)',\n",
       " '人参': '中轴(土)',\n",
       " '大枣': '中轴(土)',\n",
       " '茯苓': '中轴(土)',\n",
       " '胶饴': '中轴(土)',\n",
       " '麦门冬': '中轴(土)',\n",
       " '升麻': '中轴(土)',\n",
       " '瓜蒂': '中轴(土)',\n",
       " '鸡子黄': '中轴(土)',\n",
       " '芍药': '向下(金)',\n",
       " '柴胡': '向下(金)',\n",
       " '杏仁': '向下(金)',\n",
       " '石膏': '向下(金)',\n",
       " '五味子': '向下(金)',\n",
       " '枳实': '向下(金)',\n",
       " '铅丹': '向下(金)',\n",
       " '龙骨': '向下(金)',\n",
       " '牡蛎': '向下(金)',\n",
       " '豉': '向下(金)',\n",
       " '香豉': '收藏(水)',\n",
       " '大黄': '火',\n",
       " '芒硝': '火',\n",
       " '黄连': '火',\n",
       " '黄芩': '火',\n",
       " '泽泻': '火',\n",
       " '厚朴': '火',\n",
       " '通草': '火',\n",
       " '葱白': '火',\n",
       " '地黄': '收藏(水)',\n",
       " '麻黄': '收藏(水)',\n",
       " '葛根': '收藏(水)',\n",
       " '白术': '收藏(水)',\n",
       " '知母': '收藏(水)',\n",
       " '猪苓': '收藏(水)',\n",
       " '栀子': '收藏(水)',\n",
       " '竹叶': '收藏(水)',\n",
       " '生姜': '向上(木)',\n",
       " '附子': '向上(木)',\n",
       " '桂枝': '向上(木)',\n",
       " '半夏': '向上(木)',\n",
       " '干姜': '向上(木)',\n",
       " '粳米': '向上(木)',\n",
       " '细辛': '向上(木)',\n",
       " '阿胶': '向上(木)',\n",
       " '赤小豆': '向上(木)',\n",
       " '蜀椒': '向上(木)'}"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "yy = {}\n",
    "for i in wx1.loc[\"值\"]:\n",
    "    for j in wx1.loc[\"药物\"]:\n",
    "        for g in j:\n",
    "            yy[g] = i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'炙甘草': 120,\n",
       " '人参': 120,\n",
       " '大枣': 120,\n",
       " '茯苓': 120,\n",
       " '胶饴': 120,\n",
       " '麦门冬': 120,\n",
       " '升麻': 120,\n",
       " '瓜蒂': 120,\n",
       " '鸡子黄': 120,\n",
       " '芍药': 120,\n",
       " '柴胡': 120,\n",
       " '杏仁': 120,\n",
       " '石膏': 120,\n",
       " '五味子': 120,\n",
       " '枳实': 120,\n",
       " '铅丹': 120,\n",
       " '龙骨': 120,\n",
       " '牡蛎': 120,\n",
       " '豉': 120,\n",
       " '香豉': 120,\n",
       " '大黄': 120,\n",
       " '芒硝': 120,\n",
       " '黄连': 120,\n",
       " '黄芩': 120,\n",
       " '泽泻': 120,\n",
       " '厚朴': 120,\n",
       " '通草': 120,\n",
       " '葱白': 120,\n",
       " '地黄': 120,\n",
       " '麻黄': 120,\n",
       " '葛根': 120,\n",
       " '白术': 120,\n",
       " '知母': 120,\n",
       " '猪苓': 120,\n",
       " '栀子': 120,\n",
       " '竹叶': 120,\n",
       " '生姜': 120,\n",
       " '附子': 120,\n",
       " '桂枝': 120,\n",
       " '半夏': 120,\n",
       " '干姜': 120,\n",
       " '粳米': 120,\n",
       " '细辛': 120,\n",
       " '阿胶': 120,\n",
       " '赤小豆': 120,\n",
       " '蜀椒': 120}"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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